Muon Optimizer Integration
Nadie ha tomado este issue todavía.
Evaluación
- Dificultad
- 5/5
- Tiempo estimado
- Más de una semana
- Aptitud para principiantes
- 8/100
- Tipo de issue
- Nueva funcionalidad
- Claridad
- Bastante claro
- Estado de actividad
- Estancado
- Área
- ai, machine-learning
Línea de trabajo
This is a staged design plan for adding the Muon optimizer alongside AdamW, with no concrete file or test named in the payload. Start by locating where the optimizer is constructed in the training code, but expect the work to need the maintainers' input on the abstraction and ZeRO interaction before any patch. Done would mean a Muon-vs-AdamW comparison and configurable selection, which is well beyond a first contribution.
Escrito por el modelo de indexación a partir del texto del issue.
Descripción
🎯 Goal (What & Why)
Integrate the Muon optimizer into Fast-LLM to improve computational efficiency and downstream model performance.
Muon offers ~2x computational efficiency over AdamW and demonstrated strong performance in math and code benchmarks, see https://github.com/MoonshotAI/Moonlight/blob/master/Moonlight.pdf. The switch aims to reduce training FLOPs while maintaining or enhancing model quality.
The effort to integrate Muon involves significant work. However, the potential gains in computational efficiency and performance may justify this investment. Below is a staged approach from PoC to full integration ensures that we will only execute in full if tangible benefits are demonstrated early.
🚀 Execution Plan
Step 1: Proof of Concept (PoC)
- Approach: Implement the Muon optimizer in a minimalistic and hacky way:
- Use as much code from https://github.com/NVIDIA/Megatron-LM/pull/1428 and https://github.com/KellerJordan/Muon/tree/master as possible.
- Avoid abstracting the optimizer choice initially to speed up validation.
- Hard-code Muon as the sole optimizer in Fast-LLM, removing AdamW temporarily.
- Hard-code optimizer configurations (e.g., learning rate, weight decay) if necessary to speed up iteration.
- Focus on validating Muon's performance on our data and model architectures.
- Validation: Run a controlled experiment comparing Muon to AdamW on existing benchmarks.
- Success Criteria: Demonstrate comparable or superior performance with reduced compute cost.
Step 2: Proper Integration
- Refactoring:
- Introduce an abstraction layer for optimizers in Fast-LLM.
- Make optimizer choice configurable, allowing both Muon and AdamW.
- Implement configuration-driven optimizer selection and setup.
- Testing:
- Ensure that both Muon and AdamW can be used interchangeably.
- Maintain existing test coverage and add new tests for Muon integration.
- Interaction with ZeRO:
- Evaluate the integration with ZeRO, especially for distributed training.
- Implement necessary changes to handle the Muon optimizer in a distributed setup.
- Test for stability and performance in multi-node configurations.
Step 3: Long-Term Optimizations
- Advanced Features:
- Investigate Triton kernel development to optimize Muon further.
- Explore advanced configurations and hyper-parameter tuning for Muon.
- Documentation & Maintenance:
- Write documentation for the new optimizer integration.
- Ensure that future maintainers can understand and extend the integration.
📌 Acceptance Criteria (Must-Haves for Completion)
- The Muon optimizer is functional and tested in both standalone and distributed modes.
- The implementation includes documentation on optimizer selection and configuration.
- The PR includes a performance/impact summary comparing Muon vs. AdamW on relevant benchmarks.
- Refactoring is limited to what is necessary for integrating Muon and allowing optimizer configurability.
🛠️ Project Management
- Assign the project to the Fast-LLM project.
- Set the
Estimatefield (in days) in the GitHub project. - Use the
Sizefield to categorize the PR size (Large). - Assign an owner when opening the issue.
- Lenguaje dominante
- Python
- Estrellas
- 337
- Forks
- 52
- Métricas de merge de PR
- Sin PR fusionados en 30 d
Preparar el entorno
- Incluye un Dockerfile o un archivo de Docker Compose
- Tiene una plantilla de pull request
- Leer la guía de contribución
Primeros pasos
- Lee el issue completo y luego la guía de contribución del proyecto.
- Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
- Haz un fork del repositorio y trabaja en una rama.
- Abre un pull request que haga referencia al número del issue.
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